SmolVLM-Synthetic
| Property | Value |
|---|---|
| Parameter Count | 2.25B |
| License | Apache 2.0 |
| Architecture Base | Idefics3 |
| Tensor Type | BF16 |
What is SmolVLM-Synthetic?
SmolVLM-Synthetic is a compact multimodal AI model designed for efficient processing of combined image and text inputs. Built by HuggingFace, it represents a significant advancement in lightweight multimodal processing, utilizing innovative image compression techniques and a sophisticated visual token encoding system.
Implementation Details
The model leverages the SmolLM2 language model as its foundation and introduces several technical innovations:
- Uses 81 visual tokens to encode image patches of 384×384 pixels
- Implements advanced image compression compared to Idefics3
- Supports flexible image resolution scaling through processor configuration
- Optimized for both CPU and GPU deployment with Flash Attention 2 support
Core Capabilities
- Image captioning and visual content description
- Visual question answering
- Multi-image storytelling
- Document understanding (25% training focus)
- Chart comprehension and visual reasoning
Frequently Asked Questions
Q: What makes this model unique?
SmolVLM-Synthetic stands out for its efficient architecture that achieves impressive performance metrics while requiring minimal GPU RAM (5.02GB), making it accessible for deployment in resource-constrained environments. Its performance on benchmark tests like MMMU (38.8%) and DocVQA (81.6%) demonstrates competitive capabilities despite its compact size.
Q: What are the recommended use cases?
The model excels in tasks involving image-text interaction, including document analysis, image captioning, and visual question answering. It's particularly suitable for applications requiring efficient multimodal processing without compromising on performance. However, it should not be used for critical decision-making processes or high-stakes scenarios.
